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- Title
Distance Dependent Chinese Restaurant Processes.
- Authors
Blei, David M.; Frazier, Peter I.
- Abstract
We develop the distance dependent Chinese restaurant process, a flexible class of distributions over partitions that allows for dependencies between the elements. This class can be used to model many kinds of dependencies between data in infinite clustering models, including dependencies arising from time, space, and network connectivity. We examine the properties of the distance dependent CRP, discuss its connections to Bayesian nonparametric mixture models, and derive a Gibbs sampler for both fully observed and latent mixture settings. We study its empirical performance with three text corpora. We show that relaxing the assumption of exchangeability with distance dependent CRPs can provide a better fit to sequential data and network data. We also show that the distance dependent CRP representation of the traditional CRP mixture leads to a faster-mixing Gibbs sampling algorithm than the one based on the original formulation.
- Subjects
BAYESIAN analysis; NUMERICAL analysis; MATHEMATICAL models; ALGORITHMS; STATISTICAL sampling; CLUSTER analysis (Statistics)
- Publication
Journal of Machine Learning Research, 2011, Vol 12, Issue 8, p2461
- ISSN
1532-4435
- Publication type
Article